Fast and Accurate Classification with a Multi-Spike Learning Algorithm
for Spiking Neurons
Abstract
The formulation of efficient supervised learning algorithms for spiking neurons is complicated and remains challenging. Most existing learning methods
with the precisely firing times of spikes often result in relatively low efficiency and poor robustness
to noise. To address these limitations, we propose
a simple and effective multi-spike learning rule to
train neurons to match their output spike number
with a desired one. The proposed method will
quickly find a local maximum value (directly related to the embedded feature) as the relevant signal
for synaptic updates based on membrane potential
trace of a neuron, and constructs an error function
defined as the difference between the local maximum membrane potential and the firing threshold.
With the presented rule, a single neuron can be
trained to learn multi-category tasks, and can successfully mitigate the impact of the input noise and
discover embedded features. Experimental results
show the proposed algorithm has higher precision,
lower computation cost, and better noise robustness
than current state-of-the-art learning methods under
a wide range of learning tasks